A novel mutual information based ant colony classifier
Hang Yu, Xiaoxiao Qian, Yang Yu, Jiujun Cheng, Ying Yu, Shangce Gao · 2017
By constructing a list of IF-THEN rules, the traditional ant colony optimization (ACO) has been successfully applied on data classification with not only a promising accuracy but also a user comprehensibility. However, as the collected data to be classified usually contain large volumes and redundant features, it is challenging to further improve the classification accuracy and meanwhile reduce the computational time for ACO. This paper proposes a novel hybrid mutual information based ant colony algorithm (mr2AM+) for classification. First, a maximum relevance minimum redundancy feature selection method is used to select the most informative and discriminative attributes in a dataset. Then, we use the enhanced ACO classifier (i.e., AM+) to perform the classification. Experimental results show that the proposed mr2AM+ outperforms other seven state-of-art related classification algorithms in terms of accuracy and the size of model.